Overview

Dataset statistics

Number of variables12
Number of observations2773
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory281.6 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

avg_basket_size is highly overall correlated with gross_revenue and 1 other fieldsHigh correlation
avg_days_orders is highly overall correlated with frequencyHigh correlation
avg_products_order is highly overall correlated with avg_ticket and 1 other fieldsHigh correlation
avg_ticket is highly overall correlated with avg_products_orderHigh correlation
frequency is highly overall correlated with avg_days_ordersHigh correlation
gross_revenue is highly overall correlated with avg_basket_size and 3 other fieldsHigh correlation
orders is highly overall correlated with gross_revenue and 2 other fieldsHigh correlation
total_items is highly overall correlated with avg_basket_size and 3 other fieldsHigh correlation
total_products is highly overall correlated with avg_products_order and 3 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 27.67827332)Skewed
frequency is highly skewed (γ1 = 47.40535083)Skewed
items_returned is highly skewed (γ1 = 21.6260127)Skewed
customer_id has unique valuesUnique
recency_days has 33 (1.2%) zerosZeros
items_returned has 1481 (53.4%) zerosZeros

Reproduction

Analysis started2024-05-22 11:56:44.120033
Analysis finished2024-05-22 11:56:58.766734
Duration14.65 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

UNIQUE 

Distinct2773
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15285.281
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.3 KiB
2024-05-22T08:56:58.841966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12626.6
Q113815
median15241
Q316780
95-th percentile17950.4
Maximum18287
Range5940
Interquartile range (IQR)2965

Descriptive statistics

Standard deviation1715.1526
Coefficient of variation (CV)0.11220942
Kurtosis-1.2070293
Mean15285.281
Median Absolute Deviation (MAD)1484
Skewness0.016612507
Sum42386085
Variance2941748.4
MonotonicityNot monotonic
2024-05-22T08:56:58.960011image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15060 1
 
< 0.1%
17850 1
 
< 0.1%
13047 1
 
< 0.1%
12583 1
 
< 0.1%
13748 1
 
< 0.1%
15100 1
 
< 0.1%
15291 1
 
< 0.1%
14688 1
 
< 0.1%
17809 1
 
< 0.1%
15311 1
 
< 0.1%
Other values (2763) 2763
99.6%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18265 1
< 0.1%
18263 1
< 0.1%
18261 1
< 0.1%
18260 1
< 0.1%

gross_revenue
Real number (ℝ)

HIGH CORRELATION 

Distinct2759
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2844.942
Minimum36.56
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.3 KiB
2024-05-22T08:56:59.061990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum36.56
5-th percentile264.548
Q1628
median1169.94
Q32423.32
95-th percentile7490.982
Maximum279138.02
Range279101.46
Interquartile range (IQR)1795.32

Descriptive statistics

Standard deviation10466.686
Coefficient of variation (CV)3.6790506
Kurtosis372.80205
Mean2844.942
Median Absolute Deviation (MAD)689.02
Skewness17.097705
Sum7889024.2
Variance1.0955151 × 108
MonotonicityNot monotonic
2024-05-22T08:56:59.189454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1078.96 2
 
0.1%
1025.44 2
 
0.1%
889.93 2
 
0.1%
734.94 2
 
0.1%
331 2
 
0.1%
1314.45 2
 
0.1%
379.65 2
 
0.1%
1353.74 2
 
0.1%
598.2 2
 
0.1%
2053.02 2
 
0.1%
Other values (2749) 2753
99.3%
ValueCountFrequency (%)
36.56 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
70.02 1
< 0.1%
77.4 1
< 0.1%
84.65 1
< 0.1%
90.3 1
< 0.1%
93.35 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
140438.72 1
< 0.1%
124564.53 1
< 0.1%
117375.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%
65019.62 1
< 0.1%

recency_days
Real number (ℝ)

ZEROS 

Distinct252
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.648395
Minimum0
Maximum372
Zeros33
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size43.3 KiB
2024-05-22T08:56:59.320822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median29
Q373
95-th percentile211
Maximum372
Range372
Interquartile range (IQR)63

Descriptive statistics

Standard deviation68.422907
Coefficient of variation (CV)1.2078525
Kurtosis3.4305436
Mean56.648395
Median Absolute Deviation (MAD)23
Skewness1.8980349
Sum157086
Variance4681.6942
MonotonicityNot monotonic
2024-05-22T08:56:59.431053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.6%
4 87
 
3.1%
3 85
 
3.1%
2 85
 
3.1%
8 76
 
2.7%
10 67
 
2.4%
9 66
 
2.4%
7 65
 
2.3%
17 62
 
2.2%
22 55
 
2.0%
Other values (242) 2026
73.1%
ValueCountFrequency (%)
0 33
 
1.2%
1 99
3.6%
2 85
3.1%
3 85
3.1%
4 87
3.1%
5 43
1.6%
7 65
2.3%
8 76
2.7%
9 66
2.4%
10 67
2.4%
ValueCountFrequency (%)
372 1
 
< 0.1%
366 1
 
< 0.1%
360 1
 
< 0.1%
358 3
0.1%
354 1
 
< 0.1%
337 1
 
< 0.1%
336 2
0.1%
334 1
 
< 0.1%
333 2
0.1%
330 1
 
< 0.1%

orders
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0544537
Minimum2
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.3 KiB
2024-05-22T08:56:59.543506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median4
Q36
95-th percentile17
Maximum206
Range204
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.0729125
Coefficient of variation (CV)1.4985518
Kurtosis183.89793
Mean6.0544537
Median Absolute Deviation (MAD)2
Skewness10.623447
Sum16789
Variance82.317741
MonotonicityNot monotonic
2024-05-22T08:56:59.658569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 780
28.1%
3 498
18.0%
4 393
14.2%
5 237
 
8.5%
6 173
 
6.2%
7 138
 
5.0%
8 98
 
3.5%
9 69
 
2.5%
10 55
 
2.0%
11 54
 
1.9%
Other values (45) 278
 
10.0%
ValueCountFrequency (%)
2 780
28.1%
3 498
18.0%
4 393
14.2%
5 237
 
8.5%
6 173
 
6.2%
7 138
 
5.0%
8 98
 
3.5%
9 69
 
2.5%
10 55
 
2.0%
11 54
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%
57 1
< 0.1%

total_items
Real number (ℝ)

HIGH CORRELATION 

Distinct1631
Distinct (%)58.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1669.2236
Minimum2
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.3 KiB
2024-05-22T08:56:59.770349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile119
Q1330
median699
Q31478
95-th percentile4614
Maximum196844
Range196842
Interquartile range (IQR)1148

Descriptive statistics

Standard deviation5885.8021
Coefficient of variation (CV)3.5260717
Kurtosis486.75708
Mean1669.2236
Median Absolute Deviation (MAD)449
Skewness18.198824
Sum4628757
Variance34642666
MonotonicityNot monotonic
2024-05-22T08:56:59.955984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 11
 
0.4%
246 8
 
0.3%
150 8
 
0.3%
394 7
 
0.3%
260 7
 
0.3%
1200 7
 
0.3%
200 7
 
0.3%
300 7
 
0.3%
272 7
 
0.3%
219 7
 
0.3%
Other values (1621) 2697
97.3%
ValueCountFrequency (%)
2 1
< 0.1%
16 1
< 0.1%
17 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
25 1
< 0.1%
27 2
0.1%
30 1
< 0.1%
32 1
< 0.1%
33 2
0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
79963 1
< 0.1%
77373 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
62812 1
< 0.1%
58243 1
< 0.1%
57785 1
< 0.1%
50255 1
< 0.1%

total_products
Real number (ℝ)

HIGH CORRELATION 

Distinct468
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.76884
Minimum2
Maximum7837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.3 KiB
2024-05-22T08:57:00.088908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q134
median72
Q3143
95-th percentile400.2
Maximum7837
Range7835
Interquartile range (IQR)109

Descriptive statistics

Standard deviation277.76899
Coefficient of variation (CV)2.1404907
Kurtosis336.71947
Mean129.76884
Median Absolute Deviation (MAD)45
Skewness15.345709
Sum359849
Variance77155.614
MonotonicityNot monotonic
2024-05-22T08:57:00.205238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 40
 
1.4%
35 34
 
1.2%
26 30
 
1.1%
27 30
 
1.1%
29 28
 
1.0%
33 27
 
1.0%
31 27
 
1.0%
15 27
 
1.0%
20 26
 
0.9%
42 26
 
0.9%
Other values (458) 2478
89.4%
ValueCountFrequency (%)
2 11
0.4%
3 12
0.4%
4 16
0.6%
5 16
0.6%
6 24
0.9%
7 14
0.5%
8 13
0.5%
9 20
0.7%
10 18
0.6%
11 23
0.8%
ValueCountFrequency (%)
7837 1
< 0.1%
5670 1
< 0.1%
5095 1
< 0.1%
4577 1
< 0.1%
2698 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1673 1
< 0.1%
1636 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1918
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.106498
Minimum2.15
Maximum4453.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.3 KiB
2024-05-22T08:57:00.341040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2.15
5-th percentile4.85
Q112.42
median17.94
Q325.03
95-th percentile87.76
Maximum4453.43
Range4451.28
Interquartile range (IQR)12.61

Descriptive statistics

Standard deviation107.63141
Coefficient of variation (CV)3.3523249
Kurtosis1054.6276
Mean32.106498
Median Absolute Deviation (MAD)6.34
Skewness27.678273
Sum89031.32
Variance11584.521
MonotonicityNot monotonic
2024-05-22T08:57:00.708163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.49 7
 
0.3%
16.82 6
 
0.2%
17.66 6
 
0.2%
19.06 6
 
0.2%
17.71 5
 
0.2%
17.13 5
 
0.2%
19.44 5
 
0.2%
17.81 5
 
0.2%
20.75 5
 
0.2%
16.92 5
 
0.2%
Other values (1908) 2718
98.0%
ValueCountFrequency (%)
2.15 1
< 0.1%
2.43 1
< 0.1%
2.46 1
< 0.1%
2.51 1
< 0.1%
2.52 1
< 0.1%
2.65 1
< 0.1%
2.66 1
< 0.1%
2.71 1
< 0.1%
2.76 1
< 0.1%
2.77 1
< 0.1%
ValueCountFrequency (%)
4453.43 1
< 0.1%
1687.2 1
< 0.1%
952.99 1
< 0.1%
872.13 1
< 0.1%
841.02 1
< 0.1%
651.17 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%
615.75 1
< 0.1%
602.45 1
< 0.1%

avg_days_orders
Real number (ℝ)

HIGH CORRELATION 

Distinct1155
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.756379
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.3 KiB
2024-05-22T08:57:00.811554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134.222222
median59
Q399
95-th percentile224
Maximum366
Range365
Interquartile range (IQR)64.777778

Descriptive statistics

Standard deviation66.483798
Coefficient of variation (CV)0.84417032
Kurtosis3.6897278
Mean78.756379
Median Absolute Deviation (MAD)30
Skewness1.8311845
Sum218391.44
Variance4420.0954
MonotonicityNot monotonic
2024-05-22T08:57:00.932447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 21
 
0.8%
46 18
 
0.6%
55 17
 
0.6%
91 16
 
0.6%
31 16
 
0.6%
49 16
 
0.6%
42 15
 
0.5%
35 15
 
0.5%
21 15
 
0.5%
14 14
 
0.5%
Other values (1145) 2610
94.1%
ValueCountFrequency (%)
1 9
0.3%
2 4
0.1%
2.861538462 1
 
< 0.1%
3 6
0.2%
3.330357143 1
 
< 0.1%
3.351351351 1
 
< 0.1%
4 5
0.2%
4.191011236 1
 
< 0.1%
4.275862069 1
 
< 0.1%
4.5 1
 
< 0.1%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
364 1
 
< 0.1%
363 1
 
< 0.1%
357 2
0.1%
356 1
 
< 0.1%
355 2
0.1%
352 1
 
< 0.1%
351 2
0.1%
350 3
0.1%

frequency
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1228
Distinct (%)44.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.061834891
Minimum0.0054644809
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.3 KiB
2024-05-22T08:57:01.053390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.0054644809
5-th percentile0.0087719298
Q10.015873016
median0.024523161
Q30.042145594
95-th percentile0.11800465
Maximum34
Range33.994536
Interquartile range (IQR)0.026272578

Descriptive statistics

Standard deviation0.66955925
Coefficient of variation (CV)10.828179
Kurtosis2386.2478
Mean0.061834891
Median Absolute Deviation (MAD)0.010786897
Skewness47.405351
Sum171.46815
Variance0.44830959
MonotonicityNot monotonic
2024-05-22T08:57:01.164931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.07142857143 16
 
0.6%
0.04761904762 15
 
0.5%
0.01587301587 14
 
0.5%
0.0303030303 14
 
0.5%
0.02857142857 14
 
0.5%
0.06451612903 13
 
0.5%
0.02380952381 13
 
0.5%
0.1428571429 13
 
0.5%
0.1176470588 12
 
0.4%
0.025 12
 
0.4%
Other values (1218) 2637
95.1%
ValueCountFrequency (%)
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005509641873 1
 
< 0.1%
0.005602240896 2
0.1%
0.005617977528 1
 
< 0.1%
0.005633802817 2
0.1%
0.005681818182 1
 
< 0.1%
0.005698005698 2
0.1%
0.005714285714 3
0.1%
ValueCountFrequency (%)
34 1
 
< 0.1%
6 1
 
< 0.1%
4 1
 
< 0.1%
2 6
0.2%
1.5 1
 
< 0.1%
1.333333333 2
 
0.1%
1 4
0.1%
0.6666666667 3
0.1%
0.5522788204 1
 
< 0.1%
0.5349462366 1
 
< 0.1%

items_returned
Real number (ℝ)

SKEWED  ZEROS 

Distinct204
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.973675
Minimum0
Maximum9014
Zeros1481
Zeros (%)53.4%
Negative0
Negative (%)0.0%
Memory size43.3 KiB
2024-05-22T08:57:01.277616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39
95-th percentile96.8
Maximum9014
Range9014
Interquartile range (IQR)9

Descriptive statistics

Standard deviation290.71429
Coefficient of variation (CV)8.3123747
Kurtosis571.74568
Mean34.973675
Median Absolute Deviation (MAD)0
Skewness21.626013
Sum96982
Variance84514.798
MonotonicityNot monotonic
2024-05-22T08:57:01.383189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1481
53.4%
1 129
 
4.7%
2 117
 
4.2%
3 82
 
3.0%
4 72
 
2.6%
6 63
 
2.3%
5 55
 
2.0%
12 45
 
1.6%
8 39
 
1.4%
7 38
 
1.4%
Other values (194) 652
23.5%
ValueCountFrequency (%)
0 1481
53.4%
1 129
 
4.7%
2 117
 
4.2%
3 82
 
3.0%
4 72
 
2.6%
5 55
 
2.0%
6 63
 
2.3%
7 38
 
1.4%
8 39
 
1.4%
9 38
 
1.4%
ValueCountFrequency (%)
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%
1594 1
< 0.1%

avg_basket_size
Real number (ℝ)

HIGH CORRELATION 

Distinct1931
Distinct (%)69.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230.94908
Minimum1
Maximum6009.3333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.3 KiB
2024-05-22T08:57:01.497491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45
Q1103.33333
median172
Q3278.09524
95-th percentile583.6
Maximum6009.3333
Range6008.3333
Interquartile range (IQR)174.7619

Descriptive statistics

Standard deviation261.5335
Coefficient of variation (CV)1.1324293
Kurtosis115.89887
Mean230.94908
Median Absolute Deviation (MAD)81
Skewness7.7349287
Sum640421.79
Variance68399.773
MonotonicityNot monotonic
2024-05-22T08:57:01.605798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
86 9
 
0.3%
60 8
 
0.3%
75 8
 
0.3%
105 7
 
0.3%
82 7
 
0.3%
208 7
 
0.3%
136 7
 
0.3%
73 7
 
0.3%
197 7
 
0.3%
Other values (1921) 2695
97.2%
ValueCountFrequency (%)
1 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
11.875 1
< 0.1%
ValueCountFrequency (%)
6009.333333 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%
2082.225806 1
< 0.1%
2000 1
< 0.1%
1903.5 1
< 0.1%
1866.933333 1
< 0.1%

avg_products_order
Real number (ℝ)

HIGH CORRELATION 

Distinct1003
Distinct (%)36.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.129792
Minimum1
Maximum299.70588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.3 KiB
2024-05-22T08:57:01.726534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.5
Q110.133333
median17.307692
Q328.111111
95-th percentile56.648485
Maximum299.70588
Range298.70588
Interquartile range (IQR)17.977778

Descriptive statistics

Standard deviation18.867714
Coefficient of variation (CV)0.85259337
Kurtosis24.175983
Mean22.129792
Median Absolute Deviation (MAD)8.3076923
Skewness3.1580319
Sum61365.914
Variance355.99064
MonotonicityNot monotonic
2024-05-22T08:57:01.835833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 44
 
1.6%
14 31
 
1.1%
11 29
 
1.0%
9 26
 
0.9%
1 26
 
0.9%
10.5 25
 
0.9%
7.5 25
 
0.9%
17.5 25
 
0.9%
18 24
 
0.9%
9.5 24
 
0.9%
Other values (993) 2494
89.9%
ValueCountFrequency (%)
1 26
0.9%
1.2 1
 
< 0.1%
1.25 1
 
< 0.1%
1.333333333 2
 
0.1%
1.5 7
 
0.3%
1.568181818 1
 
< 0.1%
1.571428571 1
 
< 0.1%
1.666666667 4
 
0.1%
1.833333333 1
 
< 0.1%
2 21
0.8%
ValueCountFrequency (%)
299.7058824 1
< 0.1%
203.5 1
< 0.1%
145 1
< 0.1%
136.125 1
< 0.1%
135.5 1
< 0.1%
122 1
< 0.1%
118 1
< 0.1%
114 1
< 0.1%
110.3333333 1
< 0.1%
109.6666667 2
0.1%

Interactions

2024-05-22T08:56:57.381870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:44.301655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:45.275094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:46.398126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:47.603990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:48.842991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:49.969969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:51.422183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:52.532196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:53.776800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:55.179268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-22T08:56:58.161726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-22T08:56:48.481453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:49.590423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:51.013494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:52.192997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:53.378399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-22T08:56:57.018261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-22T08:56:46.029051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:47.291281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:48.568490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:49.688746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:51.139586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:52.272897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:53.487230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:54.643057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:56.016171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:57.114412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:58.317847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:45.104033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-22T08:56:52.354627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-22T08:56:54.779746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-22T08:56:45.184723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:46.180692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:47.505921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:48.735351image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:49.874878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:51.325940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:52.443596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:53.683296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:54.872586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:56.163491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T08:56:57.288310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-05-22T08:57:01.928801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
avg_basket_sizeavg_days_ordersavg_products_orderavg_ticketcustomer_idfrequencygross_revenueitems_returnedordersrecency_daystotal_itemstotal_products
avg_basket_size1.000-0.0430.4330.198-0.1210.0250.6020.2140.126-0.1030.7590.403
avg_days_orders-0.0431.0000.062-0.080-0.012-0.951-0.367-0.215-0.4760.225-0.344-0.300
avg_products_order0.4330.0621.000-0.6260.006-0.0710.2820.0270.016-0.1050.3120.721
avg_ticket0.198-0.080-0.6261.000-0.1410.0810.2730.1880.0910.0350.195-0.381
customer_id-0.121-0.0120.006-0.1411.0000.013-0.086-0.0580.0130.014-0.0790.013
frequency0.025-0.951-0.0710.0810.0131.0000.2550.1740.317-0.1250.2370.198
gross_revenue0.602-0.3670.2820.273-0.0860.2551.0000.4610.763-0.3730.9220.723
items_returned0.214-0.2150.0270.188-0.0580.1740.4611.0000.427-0.1860.4260.329
orders0.126-0.4760.0160.0910.0130.3170.7630.4271.000-0.4480.7040.659
recency_days-0.1030.225-0.1050.0350.014-0.125-0.373-0.186-0.4481.000-0.365-0.392
total_items0.759-0.3440.3120.195-0.0790.2370.9220.4260.704-0.3651.0000.709
total_products0.403-0.3000.721-0.3810.0130.1980.7230.3290.659-0.3920.7091.000

Missing values

2024-05-22T08:56:58.534393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-22T08:56:58.680403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_daysorderstotal_itemstotal_productsavg_ticketavg_days_ordersfrequencyitems_returnedavg_basket_sizeavg_products_order
0178505391.21372.034.01733.0297.018.151.00000034.00000040.050.9705888.735294
1130473232.5956.09.01390.0171.018.9052.8333330.02839135.0154.44444419.000000
2125836705.382.015.05028.0232.028.9026.5000000.04043150.0335.20000015.466667
313748948.2595.05.0439.028.033.8792.6666670.0179860.087.8000005.600000
415100876.00333.03.080.03.0292.0020.0000000.07500022.026.6666671.000000
5152914623.3025.014.02102.0102.045.3326.7692310.04023029.0150.1428577.285714
6146885630.877.021.03621.0327.017.2219.2631580.057377399.0172.42857115.571429
7178095411.9116.012.02057.061.088.7239.6666670.03361341.0171.4166675.083333
81531160767.900.091.038194.02379.025.544.1910110.243968474.0419.71428626.142857
9160982005.6387.07.0613.067.029.9347.6666670.0244760.087.5714299.571429
customer_idgross_revenuerecency_daysorderstotal_itemstotal_productsavg_ticketavg_days_ordersfrequencyitems_returnedavg_basket_sizeavg_products_order
561017290525.243.02.0404.0102.05.1513.00.1538460.0202.00000051.0
56191478577.4010.02.084.03.025.805.00.4000000.042.0000001.5
562017254272.444.02.0252.0112.02.4311.00.1818180.0126.00000056.0
563617232421.522.02.0203.036.011.7112.00.1666670.0101.50000018.0
563717468137.0010.02.0116.05.027.404.00.5000000.058.0000002.5
564813596697.045.02.0406.0166.04.207.00.2857140.0203.00000083.0
5654148931237.859.02.0799.073.016.962.01.0000000.0399.50000036.5
567914126706.137.03.0508.015.047.083.01.00000050.0169.3333335.0
5685135211092.391.03.0733.0435.02.514.50.3333330.0244.333333145.0
569515060301.848.04.0262.0120.02.521.04.0000000.065.50000030.0